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  • Open Access


    Novel Optimized Framework for Video Processing in IoRT Driven Hospitals

    Mani Deepak Choudhry1,*, B. Aruna Devi2, M. Sundarrajan3

    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 267-278, 2022, DOI:10.32604/iasc.2022.024024

    Abstract Internet of Remote things (IoRT) has gained recent attention and is considered as one most prominent research topics being carried out by numerous researchers worldwide. IoRT is being used in various applications and this paper mainly concentrates on the healthcare industry wherein it could be used effectively for patient monitoring. IoRT plays a crucial role in monitoring the patients in any healthcare center remotely by allowing simultaneous video transmissions possible from the emergency areas like Intensive Care Unit (ICU). Considering general scenarios, the video transmissions are done by the main use of Gaussian distribution. With… More >

  • Open Access


    Optimal Deep Learning Based Inception Model for Cervical Cancer Diagnosis

    Tamer AbuKhalil1, Bassam A. Y. Alqaralleh2,*, Ahmad H. Al-Omari3

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 57-71, 2022, DOI:10.32604/cmc.2022.024367

    Abstract Prevention of cervical cancer becomes essential and is carried out by the use of Pap smear images. Pap smear test analysis is laborious and tiresome work performed visually using a cytopathologist. Therefore, automated cervical cancer diagnosis using automated methods are necessary. This paper designs an optimal deep learning based Inception model for cervical cancer diagnosis (ODLIM-CCD) using pap smear images. The proposed ODLIM-CCD technique incorporates median filtering (MF) based pre-processing to discard the noise and Otsu model based segmentation process. Besides, deep convolutional neural network (DCNN) based Inception with Residual Network (ResNet) v2 model is More >

  • Open Access


    Analytic Beta-Wavelet Transform-Based Digital Image Watermarking for Secure Transmission

    Hesham Alhumyani1,*, Ibrahim Alrube1, Sameer Alsharif1, Ashraf Afifi1, Chokri Ben Amar1, Hala S. El-Sayed2, Osama S. Faragallah3

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4657-4673, 2022, DOI:10.32604/cmc.2022.020338

    Abstract The rapid development in the information technology field has introduced digital watermark technologies as a solution to prevent unauthorized copying and redistribution of data. This article introduces a self-embedded image verification and integrity scheme. The images are firstly split into dedicated segments of the same block sizes. Then, different Analytic Beta-Wavelet (ABW) orthogonal filters are utilized for embedding a self-segment watermark for image segment using a predefined method. ABW orthogonal filter coefficients are estimated to improve image reconstruction under different block sizes. We conduct a comparative study comparing the watermarked images using three kinds of More >

  • Open Access


    Median Filtering Detection Based on Quaternion Convolutional Neural Network

    Jinwei Wang1, 2, 3, 4, Qiye Ni3, Yang Zhang3, Xiangyang Luo2, *, Yunqing Shi5, Jiangtao Zhai3, Sunil Kr Jha3

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 929-943, 2020, DOI:10.32604/cmc.2020.06569

    Abstract Median filtering is a nonlinear signal processing technique and has an advantage in the field of image anti-forensics. Therefore, more attention has been paid to the forensics research of median filtering. In this paper, a median filtering forensics method based on quaternion convolutional neural network (QCNN) is proposed. The median filtering residuals (MFR) are used to preprocess the images. Then the output of MFR is expanded to four channels and used as the input of QCNN. In QCNN, quaternion convolution is designed that can better mix the information of different channels than traditional methods. The More >

  • Open Access


    Median Filtering Forensics Scheme for Color Images Based on Quaternion Magnitude-Phase CNN

    Jinwei Wang1, *, Yang Zhang1

    CMC-Computers, Materials & Continua, Vol.62, No.1, pp. 99-112, 2020, DOI:10.32604/cmc.2020.04373

    Abstract In the paper, a convolutional neural network based on quaternion transformation is proposed to detect median filtering for color images. Compared with conventional convolutional neural network, color images can be processed in a holistic manner in the proposed scheme, which makes full use of the correlation between RGB channels. And due to the use of convolutional neural network, it can effectively avoid the one-sidedness of artificial features. Experimental results have shown the scheme’s improvement over the state-of-the-art scheme on the accuracy of color image median filtering detection. More >

  • Open Access


    Adaptive Median Filtering Algorithm Based on Divide and Conquer and Its Application in CAPTCHA Recognition

    Wentao Ma1, Jiaohua Qin1,*, Xuyu Xiang1, Yun Tan1, Yuanjing Luo1, Neal N. Xiong2

    CMC-Computers, Materials & Continua, Vol.58, No.3, pp. 665-677, 2019, DOI:10.32604/cmc.2019.05683

    Abstract As the first barrier to protect cyberspace, the CAPTCHA has made significant contributions to maintaining Internet security and preventing malicious attacks. By researching the CAPTCHA, we can find its vulnerability and improve the security of CAPTCHA. Recently, many studies have shown that improving the image preprocessing effect of the CAPTCHA, which can achieve a better recognition rate by the state-of-the-art machine learning algorithms. There are many kinds of noise and distortion in the CAPTCHA images of this experiment. We propose an adaptive median filtering algorithm based on divide and conquer in this paper. Firstly, the More >

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